An important part of rail terminal planning and design is the modeling and analyzing of terminal congestion and traffic. Typically, congestion and traffic patterns of rail terminals are modeled using micro-simulation tools that simulate the movement of railcars on terminal tracks in precise detail. These micro-simulation tools typically require creating extraordinarily detailed virtual representations of a terminal to simulate the layout and traffic patterns of every train traveling through the terminal. For example, micro-simulation packages such as Arena® Simulation Software, typically require detailed information about the size and layout of every track in a terminal. Further, these micro-simulation tool packages are typically not intended to support the detailed modeling of terminals, and therefore only provide general modeling objects to simulate the components of a virtual terminal. Thus, simulating a terminal track typically requires making significant adaptations to general modeling objects to represent a terminal track modeling object. Accordingly, designing a virtual terminal typically requires a significant amount of time a resources. Once a virtual terminal has been created, the micro-simulation packages then typically require an immense amount of time to model traffic flow in the virtually simulated terminal, typically on the order of several days. Performing a sensitivity analysis around a parameter or variable of the terminal, or analyzing how the change in the design of a terminal would affect traffic flow, requires reiteratively modifying a parameter and then performing a full simulation of the modified virtual terminal. As a result, modeling terminal traffic and congestion patterns with micro-simulation tools typically consumes an excessive amount of time and resources. Thus, micro-simulation tools are not particularly suitable for strategic planning decisions that involve quick and high-level estimates of traffic and congestion patterns across a potentially large amount of terminals competing for investment resources.
Another drawback of micro-simulation tools is their reliance on preexisting data. Instead of simulating traffic and congestion patterns with forecasted or projected information, these tools typically run on information about tracks that have already been built and traffic data measured at some point in the past. For example, micro-simulation tools typically operate on information about the existing layout of a terminal, the size of tracks within the terminal, and traffic and congestion data measured at the terminal in the past. Rail terminals may then analyze sizing and demand performance by iteratively making incremental changes, using the preexisting track layout, and traffic and congestion data as a starting point. Thus, micro-simulation tools are typically used to model modifications or adjustments to operational terminals that have already been built. However, these micro-simulation tools are not generally capable of modeling traffic and congestion patterns of terminals that have yet to be built, i.e., still in their conceptual and functional planning or development stages. In this stage, planned terminals typically have no initial layouts on which the above-mentioned iterative analysis can be based, and only forecasted traffic and congestion data are available, because the planned terminals are still conceptual. Thus, forecast data is not particularly suitable for estimating terminal core infrastructure needs of planned terminals through micro-simulation tools, as such tools require much more information, especially in terms of terminal infrastructure layout. Accordingly, there is a need for simulation tools that can model traffic and congestion patterns of rail terminals still in their planning or development stages, on the basis of high-level historical or forecast data.
In addition to modeling traffic and congestion data, rail terminals typically model the assignment of railcars to terminal tracks to determine terminal track number and size needs. However, similarly to traffic and congestion modeling, the track assignment analysis with micro-simulation tools also rely on preexisting layouts and historical traffic and congestion data. Thus, as with traffic and congestion modeling, an initial detailed layout of preexisting terminal tracks is required in order to model the assignment of railcar blocks to terminal tracks. Further, as with traffic and congestion data, these micro-simulation tools are not generally used to model the block to track assignment of rail terminals still in their planning or development stages when no initial layouts of the terminal or historical traffic and congestion data are available. Moreover, since these micro-simulation tools typically require detailed, preexisting track information as well as iterative modifications to track information, modeling a terminal's track assignment typically consumes a significant amount of time and resources. That is, micro-simulation tools typically do not determine the number or size of tracks needed to meet a terminal's capacity demand; micro-simulation tools generally require creating a virtual terminal, reiteratively changing the number or size of tracks, and performing a full simulation of the traffic flow with each iteration. As noted above, this process is extremely time and resource intensive.
Yet another important part of rail terminal planning is the systematic collection of railcar inventory and terminal capacity data. Monitoring railcar inventory helps rail terminals balance consumer demand for transportation to specific destinations with railcar supply. By monitoring the use and storage of railcars at a given rail terminal, rail terminals may optimize the use of their railcar inventory by providing more railcars to destinations that attract the most customers and create the least amount of traffic and congestion. However, because of constraints on terminal resources, rail terminals typically only generate daily snapshots of railcar inventory and terminal capacity statistics. Frequently, the daily snapshots of inventory and capacity demand are taken on an ad hoc basis, or in the context of high-level, and highly granular reports generated once every morning. In either case, rail terminals typically do not measure railcar inventory and terminal capacity frequently enough to analyze the peaks and troughs of capacity demands throughout a given day. As a result, rail terminals typically cannot determine terminal infrastructure that will support traffic in a sustainable fashion and allow rail terminals to “design for failure”. Similarly, micro-simulation tools that use past traffic data, terminal layout, and track size information are typically too time- and resource-intensive to simulate railcar inventory and terminal capacity on a frequent basis, and cannot provide strategic answers in a time effective fashion.
Accordingly, terminal capacity planning tools of the prior art have several disadvantages, because they do not efficiently determine a terminal's track number or size needs, or model a terminal's block to track assignment, without relying on detailed preexisting track and terminal layout information. Further, terminal capacity planning tools of the prior art do not track or simulate railcar inventory and terminal capacity on a frequent basis. Other disadvantages exist.
The disclosed terminal capacity tool is directed to overcoming one or more of the disadvantages listed above.